Articles

Increased Investment in Data and Analytics

Knowledge is power, and having near real-time intelligence about your customers, products, locations, and assets is essential for any organisation aiming to remain competitive and foster growth. This necessity is particularly pronounced during periods of economic uncertainty, such as pandemics and recessions, where businesses strive not only to manage costs but also to adapt and respond to change.

For instance, a manufacturing company with multiple locations, product lines, and suppliers could leverage insights into the total cost of production by product line, combined with regional customer buying preferences, to make informed decisions about product divestment or discontinuation.​

Reflecting this trend, Gartner forecasts that worldwide IT spending will reach $5.61 trillion in 2025, marking a 9.8% increase from 2024. This growth is largely driven by investments in AI infrastructure, with spending on AI-optimized servers expected to surpass $202 billion, more than doubling expenditure on standard server hardware. Such investments underscore the critical role of technology — particularly analytics and business intelligence systems — as enablers of automation, enhanced digital experiences, and streamlined operations, all aimed at reducing costs and bolstering profitability.

Is More Spending the Answer?

Hang on a minute though! 
Isn’t this exactly what many businesses have been trying to do for years?

Digital transformation is nothing new, and neither is the need to collect and analyse swathes of data in order to fuel that transformation. Organisations have already made significant investments in Data Lakes, Data Warehouses, Business Intelligence tools, and Analytics platforms – and the people who administer them. Yet still it seems like something is missing. Still it appears that technology and business leaders are searching for the insights they need and are prepared to pump in more investment in order to find it.

But is the answer really spending more money on more tools and data stewards, scientists, and engineers to deploy and manage them? Or is there a more fundamental, basic problem that needs addressing? We think there is. We believe that one of the major reasons enterprises are struggling is because they have a fundamental data quality issue, in that they have no way of managing data quality in a consistent and automated fashion. They have the data, and they have the tools to analyse it, but the data itself is siloed, inconsistent, stale, inaccurate… or all of the above. The output of analytics and business intelligence tools can only ever be as strong as the input – and if the data is poor, so the results will be too.

The Challenge of Master Data Management (MDM)

Surely there’s a way of taking poor quality data from different sources and bringing it together in a way that is consistent, accurate and scalable?

This has been the promise of Master Data Management (MDM) systems for the past 30 years, but in reality, the process still involves a great deal of time and effort from Data Engineers whose time would be better spent elsewhere. This is one of the reasons why 75% of MDM projects still fail, because instead of automatically fixing the majority of data quality issues and empowering business users to address the exceptions, human involvement from data and IT specialists is still required most of the time.

Why Traditional MDM Systems Fall Short

The problem is not that organisations have not invested enough in managing and analysing their data estate. The issue, until now, has been that the tools available to master that data have not been fit for purpose. Traditional MDM systems can only deliver a single view or Golden Record AFTER the modelling work has been done upfront – by you. Which typically takes months, requires heavy involvement from IT staff, and is out of date by the time it’s complete, thus fuelling the disconnect between MDM and business value.

The Emergence of Modern MDM Platforms

Modern MDM platforms remove many of the barriers to creating value from data by automating or eliminating manual tasks and using advanced technologies like Graph, AI, and ML to ingest data from thousands of sources and allow the natural data model to emerge. This means leaving data in its natural state, and projecting it into your Information Models on demand. This also opens the door to having an MDM system that can host your data model in 15, 20, or 25 different shapes. They are also built for the Cloud, which means that they are easy to install, simple to set up and can scale up or down as you require. Perhaps most importantly, they break the traditional model of technology teams assuming sole responsibility for mastering data by giving business users access to the data and the power to run their own queries, to build their own information models, and interrogate data in their own ways.

A Shift Toward Augmented Data Management

In many ways, the new breed of MDM platform doesn’t look much like MDM at all. We believe that MDM as we know it will cease to exist, and we are entering a new category – that of Augmented Data Management – which seeks to simplify and speed up the process of preparing data for insights. It will still have many of the inner workings and expectations of MDM, but will capitalize on the influx of modern technology. With so much riding on the output of analytics and intelligence initiatives, it is crucial that a fully governed, reliable, compliant, and accurate supply of data is made available to the whole organisation.

Reconsidering Investment in Data and Analytics

So before building the case for increasing your investment in data and analytics, it is worth considering that it might not be more investment that is needed, but a change in focus. A solid data foundation – one that doesn’t discriminate between different types and sources of data – should always be the first priority when it comes to providing actionable intelligence and commercial insights to the business. Without it, analytics and BI tools will never deliver the outcomes demanded by organisations today.